Measurement of differential activation by heart-rate-variability for youth MDD discrimination

Abstract

Objective

Major depression disorder (MDD) is a common illness that severely limits psychosocial functioning and diminishes quality of life, particularly in young adults. Thus, it is imperial to identify MDD youth patients efficiently. This study aims to determine whether differential activation (DA) oriented recognizers can work efficiently.

Methods

This study collected heart rate variability (HRV) data and demographic information from 50 youth patients diagnosed with MDD and 53 healthy control participants. We developed six datasets, comprising baseline, stress, rest, differential activation period, Difference values between rest and stress period and combined dataset. From the provided data sets, we have developed machine learning models and also deep learning models. We then proceed to compare the performance metrics.

Results

Models that utilized DA period and integration data sets exhibited superior performance compared to others. The deep learning model based on Long Short-Term Memory model we developed demonstrated the highest performance among all the models in each data set. Specifically, in the integration dataset, the model attained a mean cross-validation accuracy of 0.806 (95 % Confidential Interval (CI) 0.785–0.827), with a mean Area under Receiver Operating Characteristic Curve of 0.805 (95 % CI 0.784–0.826) and a mean Area under the Precision-Recall Curve of 0.863 (95 % CI 0.848–0.878).

Conclusion

The combination of DA theory and HRV record provides a new insight and also an efficient way for youth MDD identification.